Disk block allocation optimization methodology with accommodation for file system cluster size greater than operating system memory page size

ABSTRACT

An apparatus is equipped with a caching function of a device driver, including a pre-fetch function that selective pre-fetches data stored in disk blocks to facilitate operation with a file system having file clusters with a cluster size greater than an underlying operating system&#39;s memory page size. The apparatus is further equipped with a disk block allocation optimization function to generate a new set of disk blocks to reallocate disk blocks for file system clusters accessed by a sequence of file accesses of interest to improve the overall access time for these file system clusters. The disk block allocation optimization function is further equipped to account for the selective pre-fetches and caching to be performed to accommodate said file system.

RELATED APPLICATIONS

This application is a continuation-in-part application to U.S. patent application, Ser. No. 09/002,891, entitled Disk Block Allocation Optimization Methodology And Applications, filed on Jan. 5, 1998, now U.S. Pat. No. 6,253,296, issued Jun. 26, 2000

which is a continuation-in-part application to U.S. patent application, Ser. No. 08/885,325, also entitled Disk Block Allocation Optimization Methodology And Applications, filed on Jun. 30, 1997,

which is a continuation-in-part application to

(a) Ser. No. 08/708,983, entitled Method and Apparatus For Improving Disk Drive Performance, filed on Sep. 6, 1996, now U.S. Pat. No. 5,802,593, issued Sep. 6, 1996;

(b) Ser. No. 08/721,826, also entitled Method and Apparatus For Improving Disk Drive Performance, filed on Sep. 27, 1996, now U.S. Pat. No. 5,890,205, issued Mar. 30, 1999; and

(c) Ser. No. 08/822,640, entitled Reducing Operating System Start Up/Boot Time Through Disk Block Relocation, filed on Mar. 21, 1997, now U.S. Pat. 5,920,896, issued Jul. 6, 1999.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to the field of computer systems. More specifically, the present invention relates to disk block allocation optimization methodology in an environment where the file system's cluster size is greater than the operating system's memory page size.

2. Background Information

In the art of computer systems, many problems involve optimizing disk block allocations. For example, in the past decade, performance of microprocessor based computer systems have increased dramatically. In particular, the operating speed of microprocessors has increased from the meager 16 MHz to well over 500 MHz. This trend is expected to continue without abatement. Correspondingly, while not as dramatic, performance of system and input/output (I/O) buses have also improved substantially, ensuring the microprocessors have adequate data to work with and kept busy. However, except for the improvement provided by buffering etc., the performance of disk drives has lagged behind. As a result, users are often deprived of the full benefit of the increased performance by the microprocessors. For example, when starting up an application or booting up an operating system, because the large majority of time is often spent on loading the application or operating system routines into memory from a disk drive, a user often does not see an appreciable difference in performance between a system equipped with a 200 MHz microprocessor or a 400 MHz microprocessor. Thus, further improvement in disk drive performance is desirable, and as will be disclosed in more detail below, the present invention provides the desired improvement in disk drive performance as well as other desirable results, which will be readily apparent to those skilled in the art, upon reading the detailed description to follow.

SUMMARY OF THE INVENTION

An apparatus is equipped with a device driver having a caching and pre-fetch function that selectively pre-fetches and cache data stored in disk blocks to facilitate operation with a file system having file clusters with a cluster size greater than an underlying operating system's memory page size. The apparatus is further equipped with a disk block allocation optimization function to generate a new set of disk blocks to reallocate disk blocks for file system clusters accessed by a sequence of file accesses of interest to improve the overall access time for these file system clusters. The disk block allocation optimization function is equipped to generate the new set of disk blocks, accounting for the selective pre-fetches performed to accommodate the file system.

BRIEF DESCRIPTION OF DRAWINGS

The present invention will be described by way of exemplary embodiments, but not limitations, illustrated in the accompanying drawings in which like references denote similar elements, and in which:

FIG. 1 is a simplified illustration of the present invention;

FIG. 2 illustrates one embodiment of the method steps of the present invention;

FIGS. 3a-3 c illustrate three embodiments of the present invention;

FIGS. 4a-4 c illustrate the operation flow of the tracer including an embodiment that traces file accesses and subsequently mapping the traced file accessed to physical disk blocks;

FIG. 5 illustrates one embodiment of the physical trace log;

FIG. 6 illustrates one embodiment of the operation flow of the reallocation optimizer;

FIG. 7 illustrates an alternative embodiment of the operation flow of the reallocation optimizer;

FIG. 8 illustrates in further details the concept of access run;

FIG. 9 illustrates in further details one embodiment of the “long access run” reallocation step;

FIG. 10 illustrates in further details one embodiment of the “short access run” reallocation step;

FIGS. 11-14 illustrate in further details one embodiment of the coalescing step;

FIG. 15 illustrates one embodiment of a computer system suitable for programming with the embodiment of the present invention illustrated in FIG. 3;

FIG. 16 illustrates yet another alternate embodiment of the operational flow of the reallocation optimizer for generating the improved alternate disk block allocation, employing a model to model a sequence of accesses against the current disk block allocation, and a number of model pruning criteria to prune the model; and

FIGS. 17a-17 e illustrate a number of model pruning criteria.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, various aspects of the present invention will be described. However, it will be apparent to those skilled in the art that the present invention may be practiced with only some or all aspects of the present invention. For purposes of explanation, specific numbers, materials and configurations are set forth in order to provide a thorough understanding of the present invention. However, it will also be apparent to one skilled in the art that the present invention may be practiced without the specific details. In other instances, well-known features are omitted or simplified in order not to obscure the present invention.

Parts of the description will be presented in terms of operations performed by a computer system, using terms such as data, flags, bits, values, characters, strings, numbers and the like, consistent with the manner commonly employed by those skilled in the art to convey the substance of their work to others skilled in the art. As well understood by those skilled in the art, these quantities take the form of electrical, magnetic, or optical signals capable of being stored, transferred, combined, and otherwise manipulated through mechanical and electrical components of the computer system; and the term computer system include general purpose as well as special purpose data processing machines, systems, and the like, that are standalone, adjunct or embedded.

Various operations will be described as multiple discrete steps in turn in a manner that is most helpful in understanding the present invention, however, the order of description should not be construed as to imply that these operations are necessarily order dependent, in particular, the order of presentation.

Referring now to FIG. 1, wherein a simplified illustration of the disk block allocation optimization of the present invention is shown. Illustrated on the top half of the figure are two simplified block representations 10 a and 10 b of a disk drive having eight blocks, block 0 through block 7. Denoted therein inside the blocks accessed by the three accesses of a simple sequence of disk accesses are the access identifiers, access 1, access 2 and access 3. The access pattern denoted in simplified block representation 10 a illustrates the manner in which the three accesses are made, under an hypothetical disk block allocation, without optimization in accordance with the present invention, whereas the access pattern denoted in simplified block representation 10 b illustrates the manner in which the same three accesses are made, under an alternate optimized disk block reallocation, wherein the data previously stored in block 7 have been moved to block 1, in accordance with the present invention.

Illustrated in the bottom half of the figure are illustrative estimates of the access times (in milli-seconds) for the three accesses under the unoptimized and optimized disk block allocations. As shown, and readily appreciated by those skilled in the art, the read times are substantially the same for all accesses under either allocation, however, under the optimized disk block allocation, significant amount of time savings will be achieved for seek and rotation times, as the block displacement between the successive accesses are much smaller, as compared to the unoptimized disk block allocation. In other words, by reallocating disk blocks, if it can be done, significant performance improvement can be achieved for a sequence of disk accesses.

As will be readily appreciated by those skilled in the art, the above simplified illustration is merely provided for ease of understanding. The problem addressed by the present invention is many times more complex than the simplified illustration. The lengths of the access sequences that are of interest are typically significantly longer. Additionally, many blocks are accessed multiple times in one sequence, and the multiple accesses are not necessarily in the same order. In other words, block x may be accessed n times in a sequence of interest, the first time after accessing block y, the second time after accessing block z, and so forth. Furthermore, not all blocks are available for re-allocation or may not be independently relocated. For example, in some of the new computer systems with large disk drives, the file system clusters (which are the atomic units of the file system) may be larger than the memory page size employed by the operating system. As a result, each file system cluster maps to multiple number of contiguous disk blocks that must be moved or relocated as a unit. However, because of the smaller operating system memory page size, the disk blocks are not necessarily successively accessed, as a file access may involve only a subset of the data of a file system cluster. Thus, the optimized disk block reallocation is seldom as simple as reallocating the disk blocks into a group of contiguous disk blocks, as illustrated by block representation 10 b.

FIG. 2 illustrates one embodiment of the method steps of the disk block allocation optimization of the present invention. The illustrated embodiment is designed for practice on a system where a file system having file clusters with a cluster size greater than the underlying operating system's memory page size. Moreover, a caching function is provided to a device driver of the disk drives to pre-fetch and caching all the disk blocks of a file cluster, whenever a disk block of a file cluster is accessed. However, as will be readily apparent to those skilled in the art, the present invention may also be practiced on systems where the file system's file clusters have a cluster size equal to or smaller than the underlying operating system's memory page size. As shown, for the illustrated embodiment, a trace is first generated for “nominal” disk block accesses resulted from a sequence of file accesses that are of interest, step 22. The trace includes either the “nominal” disk locations accessed or information that can be used to determine the “nominal” disk locations accessed. The term “nominal” refers to the fact that the trace is taken from a vantage point above the device driver, i.e. without accounting for the caching and pre-fetching being performed to accommodate the “large” cluster size file system. Next, the disk block accesses actually resulted from the sequence of file accesses, taking into account the fact that caching and pre-fetching are going to be performed to accommodate the “larger” cluster size file system, are deduced, step 23. Upon deducing the actual disk block accesses made, one or more attempts are made to generate an alternate disk block allocation for the disk blocks of the file system clusters accessed that will yield improved overall access time, as determined by a cost function, step 24. An example of a simple cost function, for illustrative purpose, is T=d×c1+c2+c3, where T=access time, d is seek distance, c1 is seek time per unit of seek distance, c2 is rotation time, and c3 is read time. If at least one of the attempts is successful, the data are revamped into the alternate disk block allocation that yields the most improvement in overall access time, step 28. Otherwise, the original disk block allocation is retained, step 30.

FIGS. 3a-3 c illustrate three exemplary embodiments of the disk block allocation optimization function of the present invention. In FIG. 3a, the disk block allocation optimization function of the present invention is packaged as standalone disk block reallocator 36, which may be invoked by a user at will to optimize a set of disk block allocations, e.g. disk blocks that are targets of a sequence of file accesses, such as those made by application 32. Disk block reallocator 36 includes tracer 38, reallocation optimizer 40 and reallocation engine 42. Reallocation optimizer 40 includes in particular simulator 41 that facilitates deduction of actual disk block accesses made for a set of nominal disk block accesses, simulating the nominal disk block accesses with the same caching and pre-fetching being performed by a target system to accommodate its “larger” cluster size file system. Tracer 38 nominally traces the disk block accesses resulted from a sequence of file accesses made by application 32, at a vantage point that is above the device driver level, as described earlier. In one embodiment, tracer 38 first nominally traces logical file accesses made by application 32, then subsequently maps the nominally traced logical file accesses to nominal physical disk blocks accessed. In an alternate embodiment, tracer 38 nominally traces the physical disk blocks accessed directly. For the illustrated embodiment, operating system 34 provides I/O read/write services for accessing disk drives, and application 32 utilizes these I/O read/write services when accessing file data stored on disk drives. Moreover, the device driver of operating system 34 for disk drivers is equipped with a caching and pre-fetching function 37, such that, whenever an access to a file system cluster is made, the cache buffers of cache function 37 are first checked to determine if the data to satisfy the requested access has been pre-fetched and buffered. If so, applicable ones of the buffered data are returned in response to the file cluster access. If not, the device driver in addition to accessing the disk drive for the requested portion of the file system cluster, also pre-fetches all other disk blocks of the file system cluster, and buffers the requested as well as the pre-fetched data in its buffers. Various buffer reallocation techniques may be used to evict and reallocate the buffers to different file system clusters at different points in time. Furthermore, operating system 34 provides event notification services, and tracer 38 leverages on these services to nominally (directly or indirectly) trace disk block accesses resulted from file accesses performed by application 32. Tracer 38 logs the nominal physical trace results in access trace 44.

Reallocation optimizer 40 is used to generate, if possible, an alternate disk block allocation for the disk blocks of the file system clusters accessed that will yield improved overall access time for the sequence of file accesses, using the nominal physical trace results logged in access trace 44. In one embodiment, the alternate disk block allocation is generated based at least in part on the order the disk blocks are accessed. As described earlier, reallocation optimizer 40 first deduces the disk block accesses actually made, using in one embodiment, a simulator 41 that can simulate disk block accesses with the same caching and pre-fetching being performed by device driver 37 of operating system 34. If successful, reallocation optimizer 40 generates reallocation vector 46 setting forth the manner in which the disk blocks should be reallocated. Where applicable, contiguity of disk blocks of a file system cluster is maintained. Reallocation engine 42 is then used to effectuate the reallocation as stipulated by reallocation vector 46.

In FIG. 3b, the disk block allocation optimization function of the present invention is packaged as disk block reallocator 36′, which is an integral part of application 32′. More specifically, for the illustrated embodiment, disk block reallocator 36′ is an integral part of installation utility 35′ of application 32′. Integrated disk block allocator 36′ is used to optimize the disk block allocation for the disk block accesses resulted from file system accesses made during start up of application 32′. Similarly, disk block reallocator 36′ includes tracer 38′, reallocation optimizer 40′ and reallocation engine 42′. Reallocation optimizer 40′ includes in particular simulator 41 ′ that facilitates deduction of actual disk block accesses made for a set of nominal disk block accesses, simulating the nominal disk block accesses with the same caching and pre-fetching being performed by a target system to accommodate its “larger” cluster size file system. Tracer 38′ nominally traces the disk block accesses resulted from the file accesses made by application 32′ during start-up. In one embodiment, tracer 38′ first nominally traces logical file accesses made by application 32′, then subsequently maps the traced logical file accesses to nominal physical disk blocks accessed. In an alternate embodiment, tracer 38′ nominally traces the physical disk blocks accessed directly. For the illustrated embodiment, operating system 34′ provides I/O read/write services for accessing disk drives, and application 32′ utilizes these I/O read/write services when accessing file data stored on disk drives. Moreover, operating system 34′ is also equipped with caching function 37′ for caching and pre-fetching data stored in disk blocks as the earlier described embodiment 34. Furthermore, operating system 34′ provides event notification services, and tracer 38′ leverages on these services to directly or indirectly trace disk accesses performed by application 32′. Tracer 38′ logs the nominal physical trace results in access trace 44′.

In like manner, reallocation optimizer 40′ is used to generate, if possible, an alternate disk block allocation for the disk blocks of the file system clusters that will yield improved overall access time for the file accesses made by application 32′ during start-up, using the nominal physical trace results logged in access trace 44. In one embodiment, the altemate disk block allocation is generated based at least in part on the order the disk blocks are accessed. As the earlier described embodiment, reallocation optimizer 40′ first deduces the actual disk block accesses based, using, in one embodiment, a simulator 41′ that can simulate disk block accesses with the same caching and pre-fetching performed in the target system to accommodate the “larger” cluster size file system. If successful, reallocation optimizer 40 generates reallocation vector 46 setting forth the manner in which the disk blocks should be reallocated. Again, where applicable, contiguity of disk blocks of the same file cluster is maintained. Reallocation engine 42 is then used to effectuate the reallocation as stipulated by reallocation vector 46.

In FIG. 3c, tracer 38″ is packaged as an integral part of operating system 34″, and the remainder of the disk block allocation optimization function of the present invention is packaged as complementary disk block reallocator 36″. Complemented disk block allocator 36″ is used to optimize the disk block allocation for the disk blocks accessed during start-up/booting of operating system 34″. Tracer 38″ again nominally traces the disk block accesses resulted from file accesses made by operating system 34″ during start-up/boot-up. Tracer 38′ logs the nominal physical trace results in an access trace (not shown). In one embodiment, tracer 38″ first traces nominal logical file accesses made by operating system 34″, then subsequently maps the nominally traced logical file accesses to nominal physical disk blocks accessed. In an alternate embodiment, tracer 38″ nominally traces the physical disk blocks accessed directly. As the earlier described embodiments, operating system 34″ is equipped with a device driver for the disk drives having the earlier described caching function 37″ that caches and pre-fetches data stored in disk blocks, as well as the event notification services.

Complementary disk block reallocator 36″ includes tracer reallocation optimizer 40″ and reallocation engine 42″. Reallocation optimizer 40″ includes in particular simulator 41″ that facilitates deduction of actual disk block accesses made for a set of nominal disk block accesses, simulating the nominal disk block accesses with the same caching and pre-fetching being performed by a target system to accommodate its “larger” cluster size file system. Reallocation optimizer 40″ is used to generate, if possible, an alternate disk block allocation for the disk blocks of the file system clusters that will yield improved overall access time for the file accesses made by operating system 34″ during start-up/boot-up, using nominal physical trace results logged in access trace. In one embodiment, the alternate disk block allocation is generated based at least in part on the order the disk blocks are accessed. Again, as the earlier described embodiment, reallocation optimizer 40″ first deduces the actual disk block accesses based, using, in one embodiment, a simulator 41″ that can simulate disk block accesses with the same caching and pre-fetching performed in the target system to accommodate the “larger” cluster size file system. If successful, reallocation optimizer 40″ generates a reallocation vector (not shown) setting forth the manner in which the disk blocks should be reallocated. Where applicable, contiguity of disk blocks of the same file system cluster is maintained. Reallocation engine 42″ is then used to effectuate the reallocation as stipulated by reallocation vector 46″.

FIGS. 4a-4 c illustrates tracer 38, including one embodiment where tracer 38 first nominally traces logical file accesses, then subsequently maps the nominally traced file accesses to nominal physical disk blocks. As shown in FIG. 4a, for the illustrated embodiment, upon invocation, tracer 38 registers itself with operating system 34, denoting its interest in file/disk accesses, in particular, the logical/physical locations accessed, step 52. Upon registering itself, tracer 38 waits for the requested information to be returned from operating system 34, and logs the access data as they are returned, as long as the trace period has not elapsed, steps 54, 56 and 58. Tracer 38 may be encoded or dynamically provided with the length of the trace period.

FIG. 4b illustrates one embodiment of a logical access trace 43. As shown, for the illustrated embodiment, logical access trace 43 includes a number of file access records 61. Each access record 61 includes an access identifier 63 identifying the access sequence number, a file identifier 65 identifying the file accessed, the file access operation performed 67, e.g. read, write or open, an offset into the file accessed 69, and the size of the data accessed 71, e.g. the number of bytes.

FIG. 4c illustrates one embodiment of the mapping operation flow of tracer 38. As shown, for the illustrated embodiment, the mapping process starts at step 51, where tracer 38 retrieves a logical file access record 61. Next, based on the file identification information 71, tracer 38 determine the disk blocks allocated to the file accessed, step 53. Then, based on access offset and size information 69 and 71, tracer 38 computes the specific disk blocks accessed, step 55. Once computed, tracer 38 outputs the physical disk block information into access trace 44, step 57. Tracer 38 repeats the above described steps until it is determined at step 59 that all logical file access records have been processed.

Either the direct or the indirect tracing approach described above may be employed to practice the present invention. The direct approach has the advantage of being simpler to implement, however it requires tracing to be re-performed each time optimization is to be performed, even though it is for the same access interest. On the other hand, the indirect approach involves additional complexity, however it has the advantage of just having to re-perform the mapping operation (as oppose to re-tracing), whenever optimization is re-performed for the same access interest.

FIG. 5 illustrates one embodiment of nominal physical access trace 44. As shown, for the illustrated embodiment, nominal physical access trace 44 includes a number of physical access records 62. Each physical access record 62 includes an access identifier 64 identifying the access sequence number (implying their order of access), and the disk blocks accessed 66.

FIG. 6 illustrates one embodiment of reallocation optimizer 40. As shown, for the illustrated embodiment, upon invocation, reallocation optimizer 40 processes the nominal trace data recorded in physical access trace 44 to deduce the actual disk blocks accessed (using a disk block access simulator as described earlier), and to obtain the current disk block allocation for the sequence of file accesses of interest, i.e. the disk locations of the file system clusters accessed, and in turn generates the cumulative access time for the current disk block allocation for the file system clusters of interest, step 72. (Simulators that can simulate the behavior of caching and pre-fetching operations are known in the art, accordingly will not be further described.) Next, reallocation optimizer 40 notes that current disk block allocation as the optimal disk block allocation, and the current cumulative access time as the optimal cumulative access time, step 74.

Having done so, reallocation optimizer 40 generates an alternate disk block allocation for the file system clusters of interest with randomized incremental changes, step 76. Randomized incremental changes may be synthesized in accordance with any number of such techniques known in the art. Using the generated alternate disk block allocation, reallocation optimizer 40 determines a new cumulative access time for the sequence of file accesses of interest, step 78. If the generated alternate disk block allocation yields improved overall access time, i.e. the new cumulative access time is better than the “optimal” cumulative access time, reallocation optimizer 40 notes the generated alternate disk block allocation as the optimal disk block allocation for the file system clusters of interest, and the new cumulative access time as the optimal cumulative access time, step 82. Otherwise, step 82 is skipped.

Steps 76, 78, and 80 and conditionally step 82 are repeated until a predetermined condition for terminating the search for alternate disk block allocation that yields improved overall access time has been met. A variety of termination conditions may be employed. For example, reallocation optimizer 40 may be encoded or dynamically provided with a parameter delimiting the “length” of search, in terms of total evaluation or elapsed time, number of alternate reallocation schemes evaluated, etc. At the conclusion of the search, reallocation optimizer 40 generates reallocation vector 46 denoting the reallocation to be performed, based on the optimal disk block allocation for the file system clusters of interest, step 86. For the illustrated embodiment, if the optimal disk block allocation was never updated, i.e. no disk block allocation yielding improved overall access time was found, reallocation vector 46 is a null vector.

FIG. 7 illustrates an alternate embodiment of reallocation optimizer 40. As shown, for the illustrated embodiment 90, upon invocation, similar to the earlier described embodiment, reallocation optimizer 40 processes the nominal trace data recorded in access trace 44 to deduce the actual disk blocks accessed (using a disk block access simulator as described earlier), and to obtain the current disk block allocation for the sequence of file accesses of interest, i.e. the disk locations of the file system clusters accessed, and in turn computes the cumulative access time for the current disk block allocation for the file system clusters, step 91. Next, unlike the earlier described embodiment, reallocation optimizer 40 coalesces the disk blocks of the file system clusters, step 92. Coalescing the disk blocks of the file system clusters may be performed in any one of a number of known techniques. One approach will be briefly described later.

Having coalesced the disk blocks of the file system clusters, reallocation optimizer 40 proceeds to analyze the deduced trace data and groups the disk blocks of the file system clusters actually accessed into access runs, step 94. FIG. 8 illustrates the concept of access runs. Illustrated therein is a hypothetical sequence of disk block accesses (142) resulting from file accesses, access 1 through access 9, against the enumerated disk blocks of the file system clusters in the order shown. For this hypothetical sequence of disk block accesses, blocks of file system clusters 2, 7 and 5 (144) are always accessed as a “run”. These blocks of file system clusters are accessed in order during access 1 through access 3, and then during access 5 through access 7. Likewise, blocks of file system clusters 8 and 10 (148) are also considered as a “run”, except it is accessed only once. Blocks of file system cluster 4 (146) is a “run” with a run length of one.

Return now to FIG. 7, having grouped the file accesses into access runs, reallocation optimizer 40 reallocates the disk blocks of the file system clusters of interest on an access run basis. For the illustrated embodiment, the “longer” access runs are reallocated first, step 96, before the “shorter” access runs are reallocated, step 98. “Longer” access runs are access runs with run lengths greater than a predetermined run length threshold (L1), whereas “shorter” access runs are access runs with run length shorter than L1. The value of L1 is application dependent, and is empirically determined. In one embodiment, L1 is set to 64 clusters. After, both the “longer” as well as the “shorter” access optimizer 40 generates reallocation vector 46 as the earlier described embodiment, step 99.

FIGS. 9-10 illustrate one embodiment each for reallocating the “longer” and “shorter” access runs. As shown in FIG. 9, for the illustrated embodiment, reallocation optimizer 40 reallocates the “longer” access runs to contiguous disk regions at both ends of a disk region, alternating between the two ends, until all “longer” access runs have been reallocated. At step 152, reallocation optimizer 40 determines if there are still “longer” access runs to be reallocated. If the determination is affirmative, for the illustrated embodiment, reallocation optimizer 40 reallocates the longest of the remaining “longer” run to the “top most” portion of the disk region, step 154. At step 156, reallocation optimizer 40 again determines if there are still “longer” access runs to be reallocated. If the determination is affirmative, for the illustrated embodiment, reallocation optimizer 40 reallocates the longest of the remaining “longer” run to the “bottom most” portion of the disk region, step 158. Steps 152, 154, 156, and 158 are repeated until all “longer” access runs have been reallocated. As steps 154 and 158 are repeated, the “top most” portion bound reallocations are reallocated in a “top down” manner, whereas the “bottom most” portion bound reallocation are reallocated in a “bottom up” manner. In other words, the “center” portion of the disk region is left unallocated at the end of the “longer” access run reallocation.

As shown in FIG. 10, for the illustrated embodiment, reallocation optimizer 40 reallocates the “shorter” access runs, by first arbitrarily picking one of the “shorter” access runs, step 162. Then the successor “shorter” access runs to the selected “shorter” access run are reallocated near the selected “shorter” access run based on their likelihood of occurrence, i.e. the frequency of occurrence of the successor “shorter” access run, steps 164 and 166. A successor access run is simply an access run that follows the selected access run. Steps 164 and 166 are then repeated until all successor access runs to the selected access run are reallocated. Then, the entire “shorter” access run reallocation process, i.e. steps 162, 164, and 166 are repeated until all “shorter” access runs have been reallocated.

The two approaches to reallocating “longer” and “shorter” access runs are complementary to each other. Together the two approaches provide the advantage of reducing the access time of the “shorter” access runs, since they are all concentrated at the “center” portion of the disk region, and the advantage of spreading the higher cost of moving to the end portions of the disk region over a larger number of accesses, since a number of successive accesses will be made at the end portions once the actuator is moved there.

Returning now to the topic of coalescing disk blocks of file system clusters of a disk drive. FIGS. 11-14 illustrate one approach for achieving the desired coalescing of disk blocks of file system clusters. As shown, for the illustrated approach, reallocation optimizer 40 first determines if both the smallest unused disk region, where an unused disk region is a disk region not accessed during the trace, and the disk region may or may not have been allocated, as well as the smallest used region are smaller than a predetermined size, step 102. The value of the predetermined size is also application dependent, and empirically determined. In one embodiment, a value of 64 clusters is also used for the predetermined size. If the determination is affirmative, reallocation optimizer 40 reallocates all or a portion of the smallest used disk region into the smallest unused disk region, step 104 (see also FIG. 12). Steps 102 and 104 are repeated until either the smallest unused disk region or the smallest used disk region is greater than or equal to the predetermined size. Together, these two steps have the effect of filling up the small “in-between” unused disk regions, and eliminate the small “in-between” used disk regions, as illustrated by FIG. 12.

Next, for the illustrated approach, reallocation optimizer 40 determines if the smallest unused disk region is smaller than the predetermined size, step 106. If the determination is affirmative, reallocation optimizer 40 reallocates one or both of the two used disk regions bounding the smallest unused disk region, by shifting one towards the other, or both towards each other, step 108 (see also FIG. 13). Steps 106 and 108 are repeated until the smallest unused disk region is greater than or equal to the predetermined size. Together, these two steps have the effect of eliminating the “in-between” small unused disk regions as illustrated by FIG. 13.

Next, for the illustrated approach, reallocation optimizer 40 determines if the smallest used disk region is smaller than the predetermined size, step 110. If the determination is affirmative, reallocation optimizer 40 reallocates the smallest used disk region, by shifting it towards the closest neighboring used disk region, step 112 (see also FIG. 14). Steps 110 and 112 are repeated until the smallest used disk region is greater than or equal to the predetermined size. Together, these two steps have the effect of eliminating any “in-between” used disk regions as illustrated by FIG. 14.

While at first brush, the two embodiments for generating an alternate disk block allocation that yields improved overall access time appear to be very different, they are really two species of a genus of approaches to practically and optimally solving the cost function of overall access time, expressed in terms of disk block allocation, i.e. Min. C{b1, b2, . . . , bn}, where C{ } is the cost function of overall access time, and (b1, b2, . . . bn) is a set of disk block allocation for a collection of file system clusters.

Skipping now to FIGS. 16 and 17a-17 d, wherein another alternate embodiment of the operational flow of reallocation optimizer 40 for generating the improved alternate disk block allocation is illustrated. For the illustrated embodiment, upon deducing the actual disk block accesses (using e.g. a simulator as described earlier), a model is employed to represent the sequence of actual disk block accesses against the current disk block allocation, and a number of model pruning criteria is employed to prune the model to derive the improved alternate disk block allocation. As illustrated, at step 252, reallocation optimizer 40 reads the actual disk location accessed, and constructs a model for the actual disk locations accessed. For the illustrated embodiment, where the unit of allocation of the file subsystem is a file cluster, reallocation optimizer 40 creates a node in the model to represent each file cluster accessed. Additionally, reallocation optimizer 40 creates a transition arc to connect two nodes to represent a successive access relationship between the clusters represented. A weight is assigned to each transition arc to represent the probability of the transition being made. The weight (probability) is computed based on the number of occurrences of the transition observed, relative to other transitions from the node. For example, if 6 and 4 transitions from node A to nodes B and C respectively are observed, the transition arc joining nodes A and B is assigned with the weight or probability of 0.6, whereas the transition arc joining nodes A and C is assigned with the weight or probability of 0.4. Other weighting approaches may be employed. Any one of a number of data structures known for storing Markovian chains may be employed to store the node and the transition arc data.

At step 254, reallocation optimizer 40 traverses the model to eliminate all forward “rejoining” transition arcs with lengths, measured in terms of the number of intermediate nodes between the connected nodes, that are shorter than a first predetermined threshold (see FIG. 17a, for ease of illustration, only one forward “rejoining” transition arc is shown). In one embodiment, the first pre-determined threshold is set to a pre-fetch size of the disk drive, which may be dynamically provided by a user prior to an optimization run, pre-configured during installation of disk block reallocator 36, or defaulted to a predetermined most likely value (e.g., the pre-fetch size of the most popular disk drive).

At step 256, reallocation optimizer 40 traverses the model to successively serialize parallel strings emanating from a node with lengths, measured in terms of nodes, that are shorter than a second pre-determined threshold (see FIG. 17b, for ease of illustration, only two parallel emanating strings are shown). Each of the eligible parallel strings (e.g. C . . . E) is successively serialized at the beginning of the longest parallel string (i.e. Q . . . T). The transition arcs emanating from the last node of each of the serialized strings (e.g. E of C . . . E) are added to the last node of the longest parallel string (i.e. T of Q . . . T). In one embodiment, one pre-determined threshold is employed as both the first and the second pre-determined threshold, and the common pre-determined threshold is provided in the same manner as described above.

At step 258, reallocation optimizer 40 traverses the model to successively serialize parallel strings joining a node with lengths, measured in terms of nodes, that are shorter than a third pre-determined threshold (see FIG. 17c, for ease of illustration, only two parallel joining strings are shown). Each of the eligible parallel strings (e.g. A . . . C) is successively serialized at the end of the longest parallel string (ie. F . . . K). The transition arcs joining the first node of each of the serialized strings (e.g. A of A . . . C) are added to the first node of the longest parallel string (i.e. F of F . . . K). In one embodiment, one common pre-determined threshold is employed for the first, the second as well as the third pre-determined threshold, and the common pre-determined threshold is provided in the same manner as described above.

At step 260, reallocation optimizer 40 traverses the model to remove all “cycle forming” backward “rejoining” transition arcs (see FIG. 17d, for ease of illustration, only one “cycle forming” backward “rejoining” transition arc is shown). At step 262, reallocation optimizer 40 traverses the model to remove all less probable incoming transition arcs, as well as all less probably outgoing transition arcs, keeping only the most probable incoming transition arc, and if applicable, the most probable outgoing transition arc, for each node with more than one outgoing or more than one incoming transition arcs (see FIG. 17e, for ease of illustration, only the outgoing case is shown). In other words, at the end of step 262, each node has at most one incoming transition arc, and at most one outgoing transition arc.

Additionally, for the illustrated embodiment, the process loops back to step 254 from step 256, 258, 260 or 262, if a reduction was actually made when applying the pruning criteria, as the reduction may create new opportunities for previously considered pruning criteria. The process continues until all remaining nodes have at most one incoming and one outgoing arc.

In general, the pruning criteria employed in the pruning strategy is devised such that the resultant model represents the portion of the traced accesses where significant performance improvement can mostly likely be obtained, given the performance characteristics of a particular class of disk drives, and the allocation restrictions imposed by the file system as well as the order of access of the traced “workload” The pruning criteria listed here are illustrative, and a number of other pruning criteria based on the characteristics of disk drives are possible.

Returning now briefly to FIGS. 3a-3 c, in one embodiment, the reallocation vector 46 specifies the disk block relocations to be performed in a conventional destination oriented manner. However, reallocation engine 36 includes a transform function for annotating reallocation vector 46 with data source information, and equipped with a source oriented data move technique that effectuates the desired disk block relocations keying off the annotated data source information, as disclosed in U.S. patent application, Ser. No. 08/885,327, entitled Source Oriented Data Move Methodology and Applications, filed Jun. 30, 1997. Furthermore, each of the three exemplary applications may further include a data block relocation de-optimization detection function for detecting de-optimizing by a “conflicting” optimization technique, as disclosed in U.S. patent application, Ser. No. 08/885,326, entitled Data Block Relocation De-optimization Detection Methodology and Applications, filed Jun. 30, 1997, now U.S. Pat. No. 5,845,297.

FIG. 15 illustrates one embodiment of a computer system suitable for practicing the present invention described above. As shown, for the illustrated embodiment, computer system 200 includes processor 202, processor bus 206, high performance I/O bus 210 and standard I/O bus 220. Processor bus 206 and high performance I/O bus 210 are bridged by host bridge 208, whereas I/O buses 210 and 220 are bridged by I/O bus bridge 212. Coupled to processor bus 206 is cache 204. Coupled to high performance I/O bus 210 are system memory 214 and video memory 216, against which video display 218 is coupled. Coupled to standard I/O bus 220 are disk drive 222 with a corresponding removable storage device, keyboard and pointing device 224 and communication interface 226.

These elements perform their conventional functions known in the art. In particular, disk drive 222 and system memory 214 are used to store a permanent and a working copy of the programming instructions for effectuating the teachings of the present invention, when executed by processor 202. The permanent copy may be pre-loaded into disk drive 222 in factory, loaded from a distribution medium, or down loaded from a remote distribution source. Disk drive 222 and system memory 214 may also be used to store similar copies of operating system 34. The constitutions of these elements are known. Any one of a number of implementations of these elements known in the art may be used to form computer system 200.

While the method and apparatus of the present invention have been described in terms of the above illustrated embodiments, those skilled in the art will recognize that the invention is not limited to the embodiments described. The present invention can be practiced with modification and alteration within the spirit and scope of the appended claims. The description is thus to be regarded as illustrative instead of restrictive on the present invention.

Thus, a disk block allocation optimization methodology with accommodation for file system's cluster size greater than operating system memory page size has been described. 

What is claimed is:
 1. An apparatus comprising (a) an execution unit for executing programming instructions; and (b) a storage medium coupled to the execution unit and having stored therein a first plurality of programming instructions to be executed by the execution unit for implementing a caching and pre-fetch function of a device driver that selective pre-fetches and caches data stored in disk blocks to facilitate operation with a file system having file clusters with a cluster size greater than an underlying operating system's memory page size, and a second plurality of programming instructions to be executed by the execution unit for implementing a disk block allocation optimization function to generate a new set of disk blocks to reallocate disk blocks for file system clusters accessed by a sequence of file accesses of interest, such that overall access time for these file system clusters will be improved, accounting for the selective pre-fetches and caching to be performed to accommodate said file system.
 2. The apparatus as set forth in claim 1, wherein the disk block allocation optimization function includes first logic for nominally tracing disk block accesses resulted from said sequence of file accesses of interest, without accounting for said selective pre-fetches performed to accommodate said file system, second logic for deducing actual disk block accesses resulted from said sequence of file accesses of interest with said selective pre-fetches being performed to accommodate said file system, and third logic for analyzing the deduced disk block accesses, and generating the new set of disk blocks.
 3. The apparatus as set forth in claim 1, wherein the second logic includes a simulator for simulating disk block accesses with said selective pre-fetches being performed to accommodate said file system.
 4. The apparatus as set forth in claim 1, wherein the third logic includes logic for generating a remapping vector mapping the disk blocks of the file system clusters accessed to the new set of disk blocks.
 5. The apparatus as set forth in claim 4, wherein the data block allocation optimization function further includes fourth logic for relocating data stored in the disk blocks of the file system clusters accessed to the new set of disk blocks using the remapping vector.
 6. The apparatus as set forth in claim 1, wherein the disk block allocation optimization function is packaged as a standalone utility that can be invoked by a user at will to optimize disk block allocation of any arbitrary sequence of file accesses.
 7. The apparatus as set forth in claim 1, wherein the disk block allocation optimization function is packaged as an integral part of an application for optimizing disk block allocations for disk blocks of file system clusters accessed by file accesses of the application during start-up of the application.
 8. The apparatus as set forth in claim 1, wherein the apparatus is a computer system.
 9. The apparatus as set forth in claim 2, wherein the third logic employs a model to model the actual disk block accesses made against the disk blocks of the file clusters, and a plurality of model pruning criteria to prune the model.
 10. The apparatus as set forth in claim 2, wherein the first logic is packaged as an integral part of said underlying operating system and the second logic is packaged as a standalone complementary function for optimizing disk block allocations for disk blocks of file system clusters accessed by file accesses of the operating system during start-up/boot time.
 11. The apparatus as set forth in claim 9, wherein the third logic models the actual disk block accesses using nodes and transition arcs joining the nodes to represent atomic allocation units accessed and access orders of the atomic allocation units accessed.
 12. The apparatus as set forth in claim 11, wherein the third logic assigns a node to represent each file cluster accessed, a transition arc joining two assigned nodes to represent a successive access relationship between the two represented file clusters, and a weight to each transition arc.
 13. The apparatus as set forth in claims 9, wherein the model pruning criteria employed by the third logic includes a model pruning criteria of conditionally eliminating a forward rejoining transition arc in accordance with a device characteristic based condition.
 14. The apparatus as set forth in claim 13, wherein the third logic eliminates a forward rejoining transition arc if the forward rejoining transition arc has an arc length that is shorter than a pre-fetch size of a class of storage device.
 15. The apparatus as set forth in claim 9, wherein the model pruning criteria employed by the third logic includes a model pruning criteria of conditionally serializing a selected one of a plurality of parallel strings of nodes emanating from a node in accordance with a device characteristic based condition.
 16. The apparatus as set forth in claim 15, wherein the reallocation optimizer selects a parallel string of nodes emanating from the node to be serialized if the parallel string of nodes has a string length that is shorter than a pre-fetch size of a class of storage device.
 17. The apparatus as set forth in claim 15, wherein the third logic serializes the selected parallel string of nodes emanating from the node by placing the selected parallel string of nodes in between the node and the longest parallel string of nodes emanating from the node.
 18. The apparatus as set forth in claim 9, wherein the model pruning criteria employed by the third logic includes a model pruning criteria of conditionally serializing a selected one of a plurality of parallel strings of nodes joining a node in accordance with a device characteristic based condition.
 19. The apparatus as set forth in claim 18, wherein the third logic selects a parallel string of nodes joining the node to be serialized if the parallel string of nodes has a string length that is shorter than a pre-fetch size of a class of storage device.
 20. The apparatus as set forth in claim 18, wherein the third logic serializes the selected parallel string of nodes joining the node by placing the selected parallel string of nodes in between the longest parallel string of nodes joining the node and the node.
 21. The apparatus as set forth in claim 9, wherein the model pruning criteria employed by the third logic includes a model pruning criteria of eliminating a cycle forming backward rejoining transition arc.
 22. The apparatus as set forth in claim 9, wherein the model pruning criteria employed by the third logic includes a model pruning criteria of eliminating all less probable transition arcs emanating from a node, keeping only the most probable transition arc emanating from the node.
 23. The apparatus as set forth in claim 9, wherein the model pruning criteria employed by the third logic includes a model pruning criteria of eliminating all less probable transition arcs joining a node, keeping only the most probable transition arc joining the node.
 24. A machine implemented method comprising: nominally tracing disk blocks accesses resulted from a sequence of file accesses of interest, without accounting for selective pre-fetches and caching performed to facilitate operation with a file system having file clusters with a cluster size greater than an underlying operating system's memory page size; deducing actual disk block accesses resulted from said sequence of file accesses of interest accounting for said selective pre-fetches and caching to be performed to accommodate said file system; and analyzing the deduced actual disk block accesses, and generating a new set of disk blocks to reallocate disk blocks for the file system clusters accessed such that overall access time for these file system clusters will be improved, based at least in part on the results of said analysis.
 25. The method as set forth in claim 24, wherein deducing comprises simulating said nominally traced disk block accesses with said selective pre-fetches being performed to accommodate said file system.
 26. The method as set forth in claim 24, wherein said generating comprises generating a remapping vector mapping the disk blocks of the file system clusters accessed to the new set of disk blocks.
 27. The method as set forth in claim 26, wherein said method further comprises relocating data stored in the disk blocks of the file system clusters accessed to the new set of disk blocks using the remapping vector.
 28. The method as set forth in claim 24, wherein said analyzing comprises employing a model to model the actual disk block accesses made against the disk blocks of the file clusters, and a plurality of model pruning criteria to prune the model.
 29. The method as set forth in claim 28, wherein said analyzing comprises modeling the actual disk block accesses using nodes and transition arcs joining the nodes to represent atomic allocation units accessed and access orders of the atomic allocation units accessed.
 30. The method as set forth in claim 29, wherein said modeling comprises assigning a node to represent each file cluster accessed, a transition arc joining two assigned nodes to represent a successive access relationship between the two represented file clusters, and a weight to each transition arc.
 31. The method as set forth in claim 28, wherein the model pruning criteria employed includes a model pruning criteria of conditionally eliminating a forward rejoining transition arc in accordance with a device characteristic based condition.
 32. The apparatus as set forth in claim 28, wherein the model pruning criteria employed includes a model pruning criteria of conditionally serializing a selected one of a plurality of parallel strings of nodes emanating from a node in accordance with a device characteristic based condition.
 33. The method as set forth in claim 28, wherein the model pruning criteria employed includes a model pruning criteria of conditionally serializing a selected one of a plurality of parallel strings of nodes joining a node in accordance with a device characteristic based condition.
 34. The method as set forth in claim 28, wherein the model pruning criteria employed includes a model pruning criteria of eliminating a cycle forming backward rejoining transition arc.
 35. The method as set forth in claim 28, wherein the model pruning criteria employed includes a model pruning criteria of eliminating all less probable transition arcs emanating from a node, keeping only the most probable transition arc emanating from the node.
 36. The method as set forth in claim 28, wherein the model pruning criteria employed includes a model pruning criteria of eliminating all less probable transition arcs joining a node, keeping only the most probable transition arc joining the node.
 37. An article of manufacture comprising a recordable medium having recorded thereon a first plurality of programming instructions for programming an apparatus to provide the apparatus with a caching and pre-fetch function of a device driver that selectively pre-fetches and caches data stored in disk blocks to facilitate operation with a file system having file clusters with a cluster size greater than an underlying operating system's memory page size, and a second plurality of programming instructions for programming the apparatus to provide the apparatus with a disk block allocation optimization function to generate a new set of disk blocks to reallocate disk blocks for file system clusters accessed by a sequence of file accesses of interest, such that overall access time for these file system clusters will be improved, accounting for the selective pre-fetches and caching to be performed to accommodate said file system.
 38. The article of manufacture of claim 37, wherein the disk block allocation optimization function includes first logic for nominally tracing disk blocks accesses resulted from said sequence of file accesses of interest, without accounting for said selective pre-fetches performed to accommodate said file system, second logic for deducing actual disk block accesses resulted from said sequence of file accesses of interest with said selective pre-fetches being performed to accommodate said file system and third logic for analyzing the deduced actual disk block accesses, and generating a new set of disk blocks to reallocate disk blocks for the file system clusters accessed such that overall access time for these file system clusters will be improved, based at least in part on the results of said analysis. 